Guiding local regression using visualisation

Dharmesh M. Maniyar, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

2 Citations (Scopus)
11 Downloads (Pure)


Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.
Original languageEnglish
Title of host publicationDeterministic and statistical methods in machine learning
EditorsJoab Winkler, Mahesan Niranjan, Neil Lawrence
Place of PublicationGermany
Number of pages12
ISBN (Print)3-540-29073-7
Publication statusPublished - 1 Dec 2005

Publication series

NameLecture Notes in Computer Science


  • regression models, classification models, large high-dimensional datasets


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